The rapid rate of architectural change has placed enormous pressure on
compiler writers to keep pace with microprocessor evolution. This
problem is compounded by the current trend to have multi-cores and
multi-threading which makes such systems increasingly difficult to
target. Also, current methods of designing computer systems will no
longer be feasible in 10-15 years time; what is needed are new
innovative approaches to architecture design that scale both with
advances in underlying technology and with future application domains.

In recent years, several papers have been published showing great
potential in constructing compilers and architectures using approaches
such as machine learning and search.

The purpose of this workshop is to promote new ideas and to present
recent developments in compiler and architecture design using machine
learning, statistical approaches, and search in order to enhance their
performance, scalability, and adaptability.